arfS4-package: Analysis of fMRI data using parameterized regions of...
In arfS4: Activated Region Fitting, fMRI data analysis.
Description
Details
Author(s)
References
See Also
Description
Activated Region Fitting (ARF) analyzes images of fMRI activation by fitting multiple Gaussian shaped models on activated areas. Hypothesis tests are performed on the parameters of the model allowing for hypotheses on location, spatial extent and amplitude.
Details
Package: arfS4
Type: Package
Version: 1.2-2
Date: 2009-02-03
License: GPL
LazyLoad: yes
Depends: methods
ARF uses multiple measures of a condition (either from a blocked design or an event related design) as input. It requires for each image a file containing beta-values from the GLM analysis and a separate file containing the standard errors of the beta-values (note that t-values with s.e. set to one can also be used). ARF assumes a directory structure with for each condition a separate directory with subdirectories 'data' and 'weights' containing the beta-values and the standard errors respectively. makeDirStruct automatically creates a directory structure given a name for the condition. After the files are in place a call to arf by default creates the average beta-images and fits the specified model.
Author(s)
Maintainer: Wouter D. Weeda <w.d.weeda@gmail.com>
References
Weeda, W.D., Waldorp, L.J., Christoffels, I., and Huizenga, H.M. (in press) Activated Region Fitting: A robust high-power method for fMRI analysis using parameterized regions of activation. Human Brain Mapping, 2009.
See Also
arfS4 documentation built on May 2, 2019, 6:14 p.m.
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Description Details Author(s) References See Also
Description
Activated Region Fitting (ARF) analyzes images of fMRI activation by fitting multiple Gaussian shaped models on activated areas. Hypothesis tests are performed on the parameters of the model allowing for hypotheses on location, spatial extent and amplitude.
Details
| Package: | arfS4 |
| Type: | Package |
| Version: | 1.2-2 |
| Date: | 2009-02-03 |
| License: | GPL |
| LazyLoad: | yes |
| Depends: | methods |
ARF uses multiple measures of a condition (either from a blocked design or an event related design) as input. It requires for each image a file containing beta-values from the GLM analysis and a separate file containing the standard errors of the beta-values (note that t-values with s.e. set to one can also be used). ARF assumes a directory structure with for each condition a separate directory with subdirectories 'data' and 'weights' containing the beta-values and the standard errors respectively. makeDirStruct automatically creates a directory structure given a name for the condition. After the files are in place a call to arf by default creates the average beta-images and fits the specified model.
Author(s)
Maintainer: Wouter D. Weeda <w.d.weeda@gmail.com>
References
Weeda, W.D., Waldorp, L.J., Christoffels, I., and Huizenga, H.M. (in press) Activated Region Fitting: A robust high-power method for fMRI analysis using parameterized regions of activation. Human Brain Mapping, 2009.
See Also
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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